The advantages of multiple classes for reducing overfitting from test set reuse

24 May 2019

Papers citing "The advantages of multiple classes for reducing overfitting from test set reuse"

9 / 9 papers shown

Title
On Statistical Learning of Branch and Bound for Vehicle Routing Optimization Andrew Naguib Waleed A. Yousef Issa Traoré Mohammed Mamun 18 0 0 15 Oct 2023
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML Lennart Purucker Lennart Schneider Marie Anastacio Joeran Beel B. Bischl Holger Hoos 28 4 0 17 Jul 2023
Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems Yuanbo Xu Yongjian Yang E. Wang LRM 91 0 0 27 Apr 2022
The Benchmark Lottery Mostafa Dehghani Yi Tay A. Gritsenko Zhe Zhao N. Houlsby Fernando Diaz Donald Metzler Oriol Vinyals 42 89 0 14 Jul 2021
MDEA: Malware Detection with Evolutionary Adversarial Learning Xiruo Wang Risto Miikkulainen AAML 17 15 0 09 Feb 2020
A New Analysis of Differential Privacy's Generalization Guarantees Christopher Jung Katrina Ligett Seth Neel Aaron Roth Saeed Sharifi-Malvajerdi Moshe Shenfeld FedML 21 47 0 09 Sep 2019
Model Similarity Mitigates Test Set Overuse Horia Mania John Miller Ludwig Schmidt Moritz Hardt Benjamin Recht 20 50 0 29 May 2019
Detecting Overfitting via Adversarial Examples Roman Werpachowski András Gyorgy Csaba Szepesvári TDI 18 45 0 06 Mar 2019
The Power of Linear Reconstruction Attacks S. Kasiviswanathan M. Rudelson Adam D. Smith AAML 57 54 0 08 Oct 2012